# -*- coding: utf-8 -*-
from skimage import io, transform
import glob
import os
import tensorflow as tf
import numpy as np
import time

path = './handGesturePic/'
# 模型保存地址
model_path='./modelSave/model.ckpt'

# 将所有的图片resize成100*100
w = 100
h = 100
c = 3


# 读取图片
def read_img(path):
    cate = [path + '/' + x for x in os.listdir(path) if os.path.isdir(path + '/' + x)]
    imgs = []
    labels = []
    for idx, folder in enumerate(cate):
        print('reading the images:%s' % (folder))
        for im in glob.glob(folder + '/*.jpg'):
            img = io.imread(im)
            img = transform.resize(img, (w, h))
            imgs.append(img)
            labels.append(idx)
    return np.asarray(imgs, np.float32), np.asarray(labels, np.int32)


data, label = read_img(path)  # data 4038*(100,100,3)  label 4038个0~5

# 打乱顺序
num_example = data.shape[0]  # 4038
arr = np.arange(num_example)  # [ 0 1 2 ... 4037]
np.random.shuffle(arr)  # 将arr乱序
data = data[arr]
label = label[arr]

# 将所有数据分为训练集和验证集
ratio = 0.8
s = np.int(num_example * ratio)
x_train = data[:s]
y_train = label[:s]
x_val = data[s:]  # 验证集
y_val = label[s:]

# -----------------构建网络----------------------
# 占位符
x = tf.placeholder(tf.float32, shape=[None, w, h, c], name='x')
y_ = tf.placeholder(tf.int32, shape=[None, ], name='y_')


# 100×100×3->100×100×32->50×50×32->50×50×64->25×25×64->25×25×128->12×12×128->12×12×128->6×6×128
def inference(input_tensor, train, regularizer):  # regularizer = tf.contrib.layers.l2_regularizer(0.0001)

    '''
    tf.nn.conv2d(input, filter, strides（步长，一般为1 ：[1, 1, 1, 1]）, padding, use_cudnn_on_gpu=None, data_format=None, name=None)
    input的张量[batch, in_height, in_width, in_channels]
    过滤器 / 内核张量 [filter_height, filter_width(filter大小）, in_channels（输入通道）, out_channels（输出通道）]

    执行以下操作：
    展平filter为一个形状为[filter_height * filter_width * in_channels, output_channels]的二维矩阵。
    从input中按照filter大小提取图片子集形成一个大小为[batch, out_height, out_width, filter_height * filter_width * in_channels]的虚拟张量。
    循环每个图片子集，右乘filter矩阵。
    '''

    with tf.variable_scope('layer1-conv1'):
        conv1_weights = tf.get_variable("weight", [5, 5, 3, 32], initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0))
        conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
        # 当padding=SAME时，输入与输出形状相同
        relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))

    with tf.name_scope("layer2-pool1"):
        pool1 = tf.nn.max_pool(relu1, ksize=[1,2,2,1],strides=[1,2,2,1],padding="VALID")

    with tf.variable_scope("layer3-conv2"):
        conv2_weights = tf.get_variable("weight", [5, 5, 32, 64], initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))
        conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))

    with tf.name_scope("layer4-pool2"):
        pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

    with tf.variable_scope("layer5-conv3"):
        conv3_weights = tf.get_variable("weight", [3, 3, 64, 128], initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))
        conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))

    with tf.name_scope("layer6-pool3"):
        pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

    with tf.variable_scope("layer7-conv4"):
        conv4_weights = tf.get_variable("weight",[3,3,128,128],initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))
        conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases))

    with tf.name_scope("layer8-pool4"):
        pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
        nodes = 6*6*128
        reshaped = tf.reshape(pool4,[-1,nodes])

    with tf.variable_scope('layer9-fc1'):
        fc1_weights = tf.get_variable("weight", [nodes, 1024],
                                      initializer=tf.truncated_normal_initializer(stddev=0.1))
        if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))
        # tf.add_to_collection向当前计算图中添加张量集合
        fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1))

        fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)
        if train: fc1 = tf.nn.dropout(fc1, 0.5)

    with tf.variable_scope('layer10-fc2'):
        fc2_weights = tf.get_variable("weight", [1024, 512],
                                      initializer=tf.truncated_normal_initializer(stddev=0.1))
        if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))
        fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1))

        fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)
        if train: fc2 = tf.nn.dropout(fc2, 0.5)

    with tf.variable_scope('layer11-fc3'):
        fc3_weights = tf.get_variable("weight", [512, 6],
                                      initializer=tf.truncated_normal_initializer(stddev=0.1))
        if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights))
        fc3_biases = tf.get_variable("bias", [6], initializer=tf.constant_initializer(0.1))
        logit = tf.matmul(fc2, fc3_weights) + fc3_biases

    return logit

# ---------------------------网络结束---------------------------
regularizer = tf.contrib.layers.l2_regularizer(0.0001)  # 返回一个执行L2正则化的函数.在损失函数上加上正则项是防止过拟合的一个重要方法
logits = inference(x, False, regularizer)

# (小处理)将logits乘以1赋值给logits_eval，定义name，方便在后续调用模型时通过tensor名字调用输出tensor
b = tf.constant(value=1, dtype=tf.float32)
logits_eval = tf.multiply(logits, b, name='logits_eval')

loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_)
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
correct_prediction = tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_)
# tf.equal Returns:A `Tensor` of type `bool`.
# tf.cast :Casts a tensor to a new type. Returns:A `Tensor` or `SparseTensor` with same shape as `x`.(shape相同只改变type)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


# 定义一个函数，按批次取数据
def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
    assert len(inputs) == len(targets)
    if shuffle:
        indices = np.arange(len(inputs))
        np.random.shuffle(indices)
    for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
        if shuffle:
            excerpt = indices[start_idx:start_idx + batch_size]
        else:
            excerpt = slice(start_idx, start_idx + batch_size)
        yield inputs[excerpt], targets[excerpt]


# 训练和测试数据，可将n_epoch设置更大一些

n_epoch=10
batch_size=64
saver=tf.train.Saver()
sess=tf.Session()
sess.run(tf.global_variables_initializer())
for epoch in range(n_epoch):
    start_time = time.time()

    # training
    train_loss, train_acc, n_batch = 0, 0, 0
    for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):

        _, err, ac = sess.run([train_op,loss,acc], feed_dict={x: x_train_a, y_: y_train_a})
        train_loss += err
        train_acc += ac
        n_batch += 1

    print("----------------epoch: %f-------------------" % epoch)
    print("   train loss: %f" % (np.sum(train_loss) / n_batch))
    print("   train acc: %f" % (np.sum(train_acc) / n_batch))

    # validation
    val_loss, val_acc, n_batch = 0, 0, 0
    for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):

        err, ac = sess.run([loss,acc], feed_dict={x: x_val_a, y_: y_val_a})
        val_loss += err
        val_acc += ac
        n_batch += 1

    print("   validation loss: %f" % (np.sum(val_loss) / n_batch))
    print("   validation acc: %f" % (np.sum(val_acc) / n_batch))
    print('\n')

    saver.save(sess, model_path)

sess.close()
